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» Modeling GA Performance for Control Parameter Optimization
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CEC
2007
IEEE
15 years 7 months ago
Towards a generic control strategy for Evolutionary Algorithms: an adaptive fuzzy-learning approach
— This paper presents a new method to generalize strategies in order to control parameters of Evolutionary Algorithms (EAs). A learning process establishes the relationship betwe...
Jorge Maturana, Frédéric Saubion
GECCO
2008
Springer
257views Optimization» more  GECCO 2008»
15 years 2 months ago
Rapid evaluation and evolution of neural models using graphics card hardware
This paper compares three common evolutionary algorithms and our modified GA, a Distributed Adaptive Genetic Algorithm (DAGA). The optimal approach is sought to adapt, in near rea...
Thomas F. Clayton, Leena N. Patel, Gareth Leng, Al...
HPCA
2006
IEEE
16 years 1 months ago
Construction and use of linear regression models for processor performance analysis
Processor architects have a challenging task of evaluating a large design space consisting of several interacting parameters and optimizations. In order to assist architects in ma...
P. J. Joseph, Kapil Vaswani, Matthew J. Thazhuthav...
GECCO
2006
Springer
332views Optimization» more  GECCO 2006»
15 years 5 months ago
Multi-objective PID-controller tuning for a magnetic levitation system using NSGA-II
This paper investigates the issue of PID-controller parameter tuning for a magnetic levitation system using the nondominated sorting genetic algorithm (NSGA-II). The magnetic levi...
Gerulf K. M. Pedersen, Zhenyu Yang
AE
2007
Springer
15 years 7 months ago
On the Design of Adaptive Control Strategies for Evolutionary Algorithms
This paper focuses on the design of control strategies for Evolutionary Algorithms. We propose a method to encapsulate multiple parameters, reducing control to only one criterion. ...
Jorge Maturana, Frédéric Saubion